Generally, decision support systems comprises domain-specific information stored within semantic knowledge base and user interfaces to allow interaction with the system for getting the right information needed to make the right decision at the right time. Domain-specific information is generally referred to as “ontologies” that are used for sharing knowledge and common understanding of a particular domain of interest, which makes communication between various beings possible and unambiguous. Communication may happen between various entities who may be human users or programmers with different level of expertise. Programmers are able to write semantic rules for semantic data in specified programming languages and store in the semantic knowledge base. However, a business user who is a non-programmer may not be able to read or write these semantic rules without the programming knowledge. This may impact the productivity of the business user as well as additional cost incurred for the team because now a programming specialist is always required for performing the reading or writing of these rules. Further, conventional decision support systems do not enable verification of the knowledge base using natural language as it is unreachable for current technology.
Therefore, there is a need for a method and a system that enables verifiable semantic rule building for semantic data using natural language interpretation and overcoming the disadvantages and limitations of the existing systems.